The Importance of Data Integration for Businesses

CIO Review Europe | Friday, June 04, 2021

A well-designed data integration solution will automate the business process and enable blended datasets without manual coding or tuning.

FREMONT, CA: In today's world, businesses generate massive amounts of data in the course of their daily operations. Some of it is generated by the company's sales, marketing, and customer service departments. Other components may result from its financial transactions or its research, development, and manufacturing activities. Thus, every source adds to a pool of data that can reveal strategically important information when analyzed as a whole.

What is data integration?

The term "data integration" refers to "the mixture of technical and business processes used to incorporate data from disparate sources into meaningful and valuable information."

Data integration creates a single, complete overview of a company's data that a business intelligence application can use to offer actionable insights based on the organization's entire data assets, regardless of the source or format. The information gathered during the integration process is frequently stored in a data warehouse.

Why it is essential for business

Business intelligence applications can obtain crucial business insights from a company's historical and current data by utilizing a detailed set of information provided by data integration. It can have a direct bottom-line influence by providing executives and managers with an in-depth understanding of the company's current operations and the opportunities and risks it faces in the marketplace.

The data integration process is frequently required when partnering with third-party organizations like suppliers, business partners, or governmental oversight agencies.

In today's IT environment, one essential application of data integration is to access data stored on legacy systems like mainframes.

How it works

Earlier, various approaches, both manual and automated, have been used for data integration. Most modern solutions employ some form of ETL (extract, transform, load) methodology.

ETL works by retrieving data from its host environment, converting it into a standardized format, and then loading it in the destination system for applications running on that system. Before the data is loaded into the destination system, the transform step generally involves a cleansing process that tries to correct errors and inadequacies in the data.

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